Manual and automated facial de-identification techniques for patient imaging with preservation of sinonasal anatomy.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 29, 2025

Abstract

PURPOSE: Facial recognition of reconstructed computed tomography (CT) scans poses patient privacy risks, necessitating reliable facial de-identification methods. Current methods obscure sinuses, turbinates, and other anatomy relevant for otolaryngology. We present a facial de-identification method that preserves these structures, along with two automated workflows for large-volume datasets.

Authors

  • Andy S Ding
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine.
  • Nimesh V Nagururu
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA.
  • Stefanie Seo
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA.
  • George S Liu
    Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA. gliu51@jh.edu.
  • Manish Sahu
    Zuse Institute Berlin, Berlin, Germany. sahu@zib.de.
  • Russell H Taylor
    Johns Hopkins University, Baltimore, MD, USA.
  • Francis X Creighton
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.

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